Values and angles visualization — Part 2

As we saw in the previous article of this series, the distribution radar chart is useful for comparing or correlating the distribution of several angles. But this mode of representation suffers from its inability — by design — to mix angular and non-angular measurements. The full radar chart (sometimes called value radar chart) overcomes this disadvantage.
This type of chart makes it possible to relate an angular measurement (which evolves over time) with a non-angular measurement (which also evolves in the same period of time).

This full radar chart, as implemented in Weather Station, displays the values of a measurement type based on an angle measurement. While this type of graph, for its daily version, is based on the simple recorded value, the historical version allows to represent the minimum, average and maximum values of the same measurement. The vertical scale, which is a linear scale, simply indicates the value of the (non-angular) measurement.

To set this chart, you must first select the modules and the measurements you want to compare. For the angle, in historical data, you can choose the most appropriate dataset (amplitude, average, maximum, median, middle, minimum or standard deviation, regarding the compilation mode you’ve set). For the measurement just choose the module and the measurement, Weather Station will use the right dataset depending on whether you are in daily or historical shortcodes wizard.
In the angle parameters, you can set the number of sectors on which you want a representation of the values. Allotment is done for 4, 8 or 16 sectors.

As an illustration, the following graph displays the visibility according to the wind source for the 2018 summer at my home. It clearly highlights a correlation between the two measurements types: when winds are coming from N-NE to E-NE the minimum visibility never falls below 5 km and its average value is almost always at its maximum (10 km). Conversely, when the winds come from the southwest quarter, the visibility can sometimes be almost zero and its average value is never at the maximum(10 km).

Like the distribution radar chart, the full radar chart is little used in meteorology. However, it allows to highlight a correlation (or a non-correlation) between two types of measures. Until we meet again in a few days for the third episode of this series, I encourage you to experiment with this type of chart to see if you find other correlations in your weather data…